Regulation of noise in the expression of a single gene

Stochasticity or the fatal ‘imperfection’ of cloning

The concept of clone is analysed with the aim of exploring the limits to which a phenotype can be said to be determined geneticaly. First of all, mutations that result from the replication, topological manipulation or lesion of DNA introduce a source of heritable variation in an otherwise identical genetic background. But more important, stochastic effects in many biological processes may superimpose a phenotypic variation which is not encoded in the genome. The source of stochasticity ranges from the random selection of alleles or whole chromosomes to be expressed in small cell populations, to fluctuations in processes such as gene expression, due to limiting amounts of the players involved. The picture emerging is that the term clone is a statistical over-simplification representing a series of individuals having essentially the same genome but capable of exhibiting wide phenotypic variation. Finally, to what extent fluctuations in biological processes, usually thought of as noise, are in fact signal is also discussed.

Measurement of SOS expression in individual Escherichia coli K-12 cells using fluorescence microscopy

Many recombination, DNA repair and DNA replication mutants have high basal levels of SOS expression as determined by a sulAp-lacZ reporter gene system on a population of cells. Two opposing models to explain how the SOS expression is distributed in these cells are: (i) the 'Uniform Expression Model (UEM)' where expression is evenly distributed in all cells or (ii) the 'Two Population Model (TPM)' where some cells are highly induced while others are not at all. To distinguish between these two models, a method to quantify SOS expression in individual bacterial cells was developed by fusing an SOS promoter (sulAp) to the green fluorescent protein (gfp) reporter gene and inserting it at attlambda on the Escherichia coli chromosome. It is shown that the fluorescence in sulAp-gfp cells is regulated by RecA and LexA. This system was then used to distinguish between the two models for several mutants. The patterns displayed by priA, dnaT, recG, uvrD, dam, ftsK, rnhA, polA and xerC mutants were explained best by the TPM while only lexA (def), lexA3 (ind-) and recA defective mutants were explained best by the UEM. These results are discussed in a context of how the processes of DNA replication and recombination may affect cells in a population differentially.

Stochastic gene expression in fluctuating environments

Stochastic mechanisms can cause a group of isogenic bacteria, each subject to identical environmental conditions, to nevertheless exhibit diverse patterns of gene expression. The resulting phenotypic subpopulations will typically have distinct growth rates. This behavior has been observed in several contexts, including sugar metabolism and pili phase variation. Under fixed environmental conditions, the net growth rate of the population is maximized when all cells are of the fastest growing phenotype, so it is unclear what fitness advantage is conferred by population heterogeneity. However, unlike ideal laboratory conditions, natural environments tend to fluctuate, either periodically or randomly. Here we use a stochastic population model to show that, during growth in such fluctuating environments, a dynamically heterogenous bacterial population can sometimes achieve a higher net growth rate than a homogenous one. By using stochastic mechanisms to sample several distinct phenotypes, the bacteria are able to anticipate and take advantage of sudden changes in their environment. However, this heterogeneity is beneficial only if the bacterial response rate is sufficiently low. Our results could be useful in the design of artificial evolution experiments and in the optimization of fermentation processes.

Stochastic gene expression, disruption of tissue averaging effects and cancer as a disease of development

Despite the extensive literature describing the somatic genetic alterations in cancer cells, the precise origins of cancer cells remain controversial. In this article, I suggest that the etiology of cancer and the generation of genetic instability in cancer cells should be considered in the light of recent findings on both the stochastic nature of gene expression and its regulation at tissue level. By postulating that gene expression is intrinsically probabilistic and that stabilization of gene expression arises by cellular interactions in "morphogenetic fields", development and cellular differentiation can be rethought in an evolutionary perspective. In particular, this article proposes that disruptions of cellular interactions are the initial source of abnormal gene expression in cancer cells. Consequently, cancer phenotypes such as genetic and epigenetic instabilities, and also the presence of cells with stem cell-like properties, may result from inaccurate and aberrant patterns of gene expression generated by microenvironmental alterations. Finally, the therapeutic implications of this view are discussed.

Optimal path to epigenetic switching

We use large deviation methods to calculate rates of noise-induced transitions between states in multistable genetic networks. We analyze a synthetic biochemical circuit, the toggle switch, and compare the results to those obtained from a numerical solution of the master equation.

Noisy signal amplification in ultrasensitive signal transduction

Because intracellular processes are inherently noisy, stochastic reactions process noisy signals in cellular signal transduction. One essential feature of biological signal transduction systems is the amplification of small changes in input signals. However, small random changes in the input signals could also be amplified, and the transduction reaction can also generate noise. Here, we show theoretically how the abrupt response of ultrasensitive signal-transduction reactions results in the generation of large inherent noise and the high amplification of input noise. The inherently generated noise propagates with amplification through intracellular molecular network. We discuss how the contribution of such transmitted noise can be shown experimentally. Our results imply that the switch-like behavior of signal transduction could be limited by noise; however, high amplification reaction could be advantageous to generate large noise, which would be essential to maintain behavioral variability.

Frequency domain chemical Langevin analysis of stochasticity in gene transcriptional regulation

We present a frequency domain Langevin approach for stochastic analysis that remains valid for many important gene circuit elements even as molecular populations approach zero. We begin by considering the case of low-rate transcription and show that the previously reported shot noise representation is exact at all mRNA population levels for a constant transcription rate. Next, we consider transcriptional control through protein-DNA interactions at an operator site within the gene promoter region. This analysis results in expressions for the dynamics and noise behavior of this important gene sub-circuit, including the spectral density of the intrinsic operator noise and the processing of extrinsic noise by this transcriptional regulation system. This analysis shows that mRNA synthesis noise is composed of wideband shot noise and band-limited operator binding generated noise components. We find that the bandwidth of operator noise and its ultimate effect on total mRNA and protein noise is controlled by operator binding and unbinding dynamics. The most substantial impact of the operator noise is seen at transcription rates just above basal expression. This analysis captures the full behavior of this transcriptional regulation system, and points to potentially serious flaws in simplified mathematical relationships often used to model transcriptional regulation.

Continuous control in bacterial regulatory circuits

We show that for two well-characterized regulatory circuits in Escherichia coli, Tn10 tetracycline resistance and porin osmoregulation, the transcriptional outputs in individual cells are graded functions of the applied stimuli. These systems are therefore examples of naturally occurring regulatory circuits that exhibit continuous control of transcription. Surprisingly, however, we find that porin osmoregulation is open loop; i.e., the porin expression level does not feed back into the regulatory circuit. This mode of control is particularly interesting for an organism such as E. coli, which proliferates in diverse environments, and raises important questions regarding the biologically relevant inputs and outputs for this system.

Intrinsic noise, gene regulation and steady-state statistics in a two-gene network

The intrinsic noise in a two-gene network model is analysed. The technique of the Fokker-Planck approximation is used to investigate the statistics of noise when the system state is near a stable equilibrium. This is called also the steady-state statistics. The relative size of noise is measured by the Fano factor that is defined as the ratio of the variance to the mean. Our main result shows that in general, the noise control in a two-gene network might be a very complicated process, but for the repressor-repressor system that is a very important case in investigating the genetic switch, the relative size of noise, i.e. the Fano factor, must be bigger than one for both the repressor proteins.

Nonspecific binding of the OR repressors CI and Cro of bacteriophage λ

We estimate the Gibbs free energy for nonspecific binding (DeltaGNSB) to the Escherichia coli DNA for two regulatory proteins of the lambda phage, CI and Cro. By means of a statistical-mechanical approach, we calculate the cI and cro activities associated with the operator OR of an introduced lambda phage genome (prophage). In this statistical model we apply in vitro-measured binding free energies to fit in vivo experimental data for cI and cro activities, respectively, where DeltaGNSB is introduced as a free (fitting) parameter. Without nonspecific binding included in the model, the quality of the description is fairly poor, whereas data are nicely correlating with our model with nonspecific binding included over the entire data range. The obtained values of DeltaGNSB are -4.1+/-0.9 kcal/mol, for CI, and -4.2+/-0.8 kcal/mol, for Cro. In particular, in a lysogen (approximately 250 CI monomers per cell) we conclude that 86% of the total CI in the cell is nonspecifically bound, leaving on average around 10 CI dimers freely available in the E. coli cytoplasma. These findings corroborate the view that due to low free cellular particle numbers a dynamical analysis of genetic regulation at OR and comparable systems should include a stochastic component. In addition, we perform a stability analysis of the OR system in the presence of nonspecific binding.

Tracking operator state fluctuations in gene expression in single cells

We report the results of operator state fluctuations in gene expression for the entire bacterial growth cycle, using single-cell analysis and synthetic unregulated and negative-feedback transcription regulatory gene circuits. In the unregulated circuit, during the cell cycle, we observe a crossover from log-normal-to-normal distribution of expressed proteins and an unusual linear dependence of their standard deviation on the mean gene expression levels. With negative-feedback circuits we find the existence of bimodality as the cell cycle progresses. We suggest that such long-tail and bimodal distributions may be used as selection mechanisms in developmental switches and for assigning cell identity.

Emergence of complex dynamics in a simple model of signaling networks

Various physical, social, and biological systems generate complex fluctuations with correlations across multiple time scales. In physiologic systems, these long-range correlations are altered with disease and aging. Such correlated fluctuations in living systems have been attributed to the interaction of multiple control systems; however, the mechanisms underlying this behavior remain unknown. Here, we show that a number of distinct classes of dynamical behaviors, including correlated fluctuations characterized by 1/f scaling of their power spectra, can emerge in networks of simple signaling units. We found that, under general conditions, complex dynamics can be generated by systems fulfilling the following two requirements, (i) a "small-world" topology and (ii) the presence of noise. Our findings support two notable conclusions. First, complex physiologic-like signals can be modeled with a minimal set of components; and second, systems fulfilling conditions i and ii are robust to some degree of degradation (i.e., they will still be able to generate 1/f dynamics).

Bridging the gap between stochastic and deterministic regimes in the kinetic simulations of the biochemical reaction networks

The biochemical reaction networks include elementary reactions differing by many orders of magnitude in the numbers of molecules involved. The kinetics of reactions involving small numbers of molecules can be studied by exact stochastic simulation. This approach is not practical for the simulation of metabolic processes because of the computational cost of accounting for individual molecular collisions. We present the "maximal time step method," a novel approach combining the Gibson and Bruck algorithm with the Gillespie tau-leap method. This algorithm allows stochastic simulation of systems composed of both intensive metabolic reactions and regulatory processes involving small numbers of molecules. The method is applied to the simulation of glucose, lactose, and glycerol metabolism in Escherichia coli. The gene expression, signal transduction, transport, and enzymatic activities are modeled simultaneously. We show that random fluctuations in gene expression can propagate to the level of metabolic processes. In the cells switching from glucose to a mixture of lactose and glycerol, random delays in transcription initiation determine whether lactose or glycerol operon is induced. In a small fraction of cells severe decrease in metabolic activity may also occur. Both effects are epigenetically inherited by the progeny of the cell in which the random delay in transcription initiation occurred.

A genetic circuit amplifier: design and simulation

A genetic circuit amplifier is designed using an electronic inverting amplifier as a starting point. Two simulation methods are used to analyze circuit performance in terms of the impulse and sinusoidal responses of electrical engineering. The first method is an exact stochastic simulation based on a kinetic model of the circuit. The second method incorporates statistical thermodynamic analysis. The simulations are used to analyze amplifier performance in response to classical systems analysis stimuli: impulses and sine waves. Degradation reactions, analogous to leakage off circuit capacitors, are found to have considerable impact on circuit response. For the nonlinear gain element used in our exemplary circuit, the selection of bias level based on controlling protein degradation rate plays an important role in determining circuit behavior. A parameter without electronic analog, the circuit plasmid copy number, is crucial to circuit operation. These simulations suggest that the copy number must be less than 50 for desired circuit operation.

The Ambush Hypothesis: Hidden Stop Codons Prevent Off-Frame Gene Reading

Coding sequences lack stop codons, but many stops appear off-frame. Off-frame stops (stops in -1 and +1 shifted reading frames, termed hidden stops) terminate frame-shifted translation, potentially decreasing energy, and resource waste on nonfunctional proteins. Benefits may include reduced waste elimination costs and avoidance of potentially cytotoxic frame-shifted products. Our "ambush" hypothesis suggests that hidden stops are sometimes selected for. Codons of many amino acids can contribute to hidden stops, depending on the synonymous position state and adjacent codons. In vertebrate mitochondria, 31.75% of all amino acid combinations can form hidden stops. Codons with more potential to form hidden stops have greater usage frequency and bias in their favor among synonymous codons. Among primates, predicted mitochondrial rRNA secondary structure stability correlates negatively with the number of hidden stops in the mitochondrial genome. The taxonomic distribution of genetic codes suggests that +1 frameshifts might be more frequent than -1 frameshifts. This is confirmed by analyses of primate mitochondrial genomes: species with unstable rRNAs have more +1 stops, but the correlation is weak for -1 stops. High hidden stop density seems to be an adaptation in species with slippage prone ribosomes (unstable rRNAs). Hidden stops may thus compensate for reduced efficiency of some parts of the biosynthetic machinery. Some experimental data confirm our hypothesis: gene expression increases with the experimentally manipulated number of stops in the promoter region of a gene, suggesting biotechnological applications.

Simultaneously Monitoring Gene Expression Kinetics and Genetic Noise in Single Cells by Optical Well Arrays

An optical fiber based well array platform was used for simultaneous, dynamic gene expression monitoring from hundreds of individual live Escherichia coli cells carrying promoter-fluorescent reporter gene fusions. High information content about gene expression kinetics and cell-to-cell gene expression variability can be collected from a single experiment. These data are invaluable for investigating gene regulation and gene networks as well as for systems biology applications.

Genomics view of gonadotrope signaling circuits

Gonadotropin-releasing hormone (GnRH) binds to the pituitary GnRH receptor to activate signal transduction cascades that ultimately modulate gonadotropin biosynthesis. Comprehensive studies of the GnRH-activated gene program in the LbetaT2 gonadotrope cell line have greatly increased our knowledge of the number of early and intermediate gene transcripts that are modulated by GnRH. Among the classes of gene induced are several whose protein products provide feedback to various levels of signaling pathways, suggesting that gene induction forms an integral component of signal transduction and contributes to longer-timescale feedback and feedforward loops. High-throughput quantitative genomic studies, mathematical modeling and biochemical studies are beginning to delineate the organization and function of the signal-decoding and logic circuit modules of the gonadotrope's signal transduction network.

Noise, delays, robustness, canalization and all that

A biological system such as a developing embryo can withstand many perturbations. What is the basis of this robustness both against noise and mutation? Recent advances in modeling may throw new light on this old problem. First, recent theoretical and experimental work clearly demonstrates the importance of noise and time delays for the proper functioning of genetic networks: noise and delays are simply part of the normal operating constraints. By contrast, sweeping statements have been made recently about a so-called 'robustness' of biological processes, based on work that neglects noise and delays completely. I submit that studying the stability of complex biological systems with such omissions is an unnecessary, inadequate and potentially disastrous simplification. I review the existing alternatives and propose using them to construct a modeling framework that overcomes all serious limitations.

Multivariate analysis of noise in genetic regulatory networks

Stochasticity is an intrinsic property of genetic regulatory networks due to the low copy numbers of the major molecular species, such as, DNA, mRNA, and regulatory proteins. Therefore, investigation of the mechanisms that reduce the stochastic noise is essential in understanding the reproducible behaviors of real organisms and is also a key to design synthetic genetic regulatory networks that can reliably work. We use an analytical and systematic method, the linear noise approximation of the chemical master equation along with the decoupling of a stoichiometric matrix. In the analysis of fluctuations of multiple molecular species, the covariance is an important measure of noise. However, usually the representation of a covariance matrix in the natural coordinate system, i.e. the copy numbers of the molecular species, is intractably complicated because reactions change copy numbers of more than one molecular species simultaneously. Decoupling of a stoichiometric matrix, which is a transformation of variables, significantly simplifies the representation of a covariance matrix and elucidates the mechanisms behind the observed fluctuations in the copy numbers. We apply our method to three types of fundamental genetic regulatory networks, that is, a single-gene autoregulatory network, a two-gene autoregulatory network, and a mutually repressive network. We have found that there are multiple noise components differently originating. Each noise component produces fluctuation in the characteristic direction. The resulting fluctuations in the copy numbers of the molecular species are the sum of these fluctuations. In the examples, the limitation of the negative feedback in noise reduction and the trade-off of fluctuations in multiple molecular species are clearly explained. The analytical representations show the full parameter dependence. Additionally, the validity of our method is tested by stochastic simulations.

Modeling a synthetic multicellular clock: repressilators coupled by quorum sensing

Diverse biochemical rhythms are generated by thousands of cellular oscillators that somehow manage to operate synchronously. In fields ranging from circadian biology to endocrinology, it remains an exciting challenge to understand how collective rhythms emerge in multicellular structures. Using mathematical and computational modeling, we study the effect of coupling through intercell signaling in a population of Escherichia coli cells expressing a synthetic biological clock. Our results predict that a diverse and noisy community of such genetic oscillators interacting through a quorum-sensing mechanism should self-synchronize in a robust way, leading to a substantially improved global rhythmicity in the system. As such, the particular system of coupled genetic oscillators considered here might be a good candidate to provide the first quantitative example of a synchronization transition in a population of biological oscillators.

Dynamics of gene regulatory networks with cell division cycle

This paper focuses on modeling and analyzing the nonlinear dynamics of gene regulatory networks with the consideration of a cell division cycle with duplication process of DNA, in particular for switches and oscillators of synthetic networks. We derive two models that may correspond to the eukaryotic and prokaryotic cells, respectively. A biologically plausible three-gene model ( lac, tetR, and cI ) and a repressilator as switch and oscillator examples are used to illustrate our theoretical results. We show that the cell cycle may play a significant role in gene regulation due to the nonlinear dynamics of a gene regulatory network although gene expressions are usually tightly controlled by transcriptional factors.

Synthetic biology evolves

Synthetic biology is advancing rapidly as biologists, physicists and engineers are combining their efforts to understand and program cell function. By characterizing isolated genetic components or modules, experimentalists have paved the way for more quantitative analyses of genetic networks. A recent paper presents a method of computational, or in silico, evolution in which a set of components can evolve into networks that display desired behaviors. An integrated approach that includes a strategy of in silico design by evolution, together with efforts exploiting directed evolution in vivo, is likely to be the next step in the evolution of synthetic biology.

Intrinsic and external noise in an auto-regulatory genetic network

A single gene auto-regulatory network is analysed. The main goal is to investigate the effects of the negative and positive feedbacks on the intrinsic and external noises. The central finding of this paper is that: for the intrinsic noise, both the negative and positive feedback regulations increase the fluctuation strength of mRNA levels (where the fluctuation strength is measured by the Fano factor for both the fluctuations of mRNAs and proteins), and the negative feedback decreases, but the positive feedback increases, the fluctuation strength of proteins; for the external noise, the negative feedback not only increase the fluctuation strength of mRNA levels but also the fluctuation strength of proteins, and though the effect of the positive feedback on the fluctuation strength of mRNA levels depends on the size of positive feedback parameter k, the positive feedback must decrease the fluctuation strength of proteins.

Effects of stochasticity in models of the cell cycle: from quantized cycle times to noise-induced oscillations

Noise and fluctuations are ubiquitous in living systems. Still, the interaction between complex biochemical regulatory systems and the inherent fluctuations ('noise') is only poorly understood. As a paradigmatic example, we study the implications of noise on a recently proposed model of the eukaryotic cell cycle, representing a complex network of interactions between several genes and proteins. The purpose of this work is twofold: First, we show that the inclusion of noise into the description of the system accounts for several recent experimental findings, as e.g. the existence of quantized cycle times in wee1- cdc25delta double-mutant cells of fission yeast. In the main part, we then focus on more general aspects of the interplay between noise and the dynamics of the system. In particular, we demonstrate that a stochastic description leads to qualitative changes in the dynamics, such as the emergence of noise-induced oscillations. These findings will be discussed in the light of an ongoing debate on models of cell division as limit-cycle oscillators versus checkpoint mechanisms.

Noise-reduction through interaction in gene expression and biochemical reaction processes

We demonstrate that interaction in gene expression and biochemical reaction processes has a significant influence on reducing fluctuations. Especially, we have found that the interaction between synthesized proteins and background molecules can reduce the fluctuation level in gene expression, which is a counter example to the intuition that background factors disturb information processing in genetic networks by increasing the noise level. This fact also indicates that the macromolecular crowding observed in actual cells can contribute to reduce the noise level. In addition, the noise-reduction phenomenon is not limited to the interaction between the proteins and background molecules, but can be applied to other reactions such as a dimerization process and the coupling of reactions with large fluctuations by intrinsic noise. Finally, on the basis of these results, we propose a new and plausible method for reducing the fluctuations generated in synthesized genetic networks, and also discuss the applicability of this method to the stabilization of system dynamics by using a toggle switch model.

Control of stochasticity in eukaryotic gene expression

Noise, or random fluctuations, in gene expression may produce variability in cellular behavior. To measure the noise intrinsic to eukaryotic gene expression, we quantified the differences in expression of two alleles in a diploid cell. We found that such noise is gene-specific and not dependent on the regulatory pathway or absolute rate of expression. We propose a model in which the balance between promoter activation and transcription influences the variability in messenger RNA levels. To confirm the predictions of our model, we identified both cis- and trans-acting mutations that alter the noise of gene expression. These mutations suggest that noise is an evolvable trait that can be optimized to balance fidelity and diversity in eukaryotic gene expression.

Programmable cells: interfacing natural and engineered gene networks

Novel cellular behaviors and characteristics can be obtained by coupling engineered gene networks to the cell's natural regulatory circuitry through appropriately designed input and output interfaces. Here, we demonstrate how an engineered genetic circuit can be used to construct cells that respond to biological signals in a predetermined and programmable fashion. We employ a modular design strategy to create Escherichia coli strains where a genetic toggle switch is interfaced with: (i) the SOS signaling pathway responding to DNA damage, and (ii) a transgenic quorum sensing signaling pathway from Vibrio fischeri. The genetic toggle switch endows these strains with binary response dynamics and an epigenetic inheritance that supports a persistent phenotypic alteration in response to transient signals. These features are exploited to engineer cells that form biofilms in response to DNA-damaging agents and cells that activate protein synthesis when the cell population reaches a critical density. Our work represents a step toward the development of "plug-and-play" genetic circuitry that can be used to create cells with programmable behaviors.

Noise minimization in eukaryotic gene expression

All organisms have elaborate mechanisms to control rates of protein production. However, protein production is also subject to stochastic fluctuations, or “noise.” Several recent studies in Saccharomyces cerevisiae and Escherichia coli have investigated the relationship between transcription and translation rates and stochastic fluctuations in protein levels, or more generally, how such randomness is a function of intrinsic and extrinsic factors. However, the fundamental question of whether stochasticity in protein expression is generally biologically relevant has not been addressed, and it remains unknown whether random noise in the protein production rate of most genes significantly affects the fitness of any organism. We propose that organisms should be particularly sensitive to variation in the protein levels of two classes of genes: genes whose deletion is lethal to the organism and genes that encode subunits of multiprotein complexes. Using an experimentally verified model of stochastic gene expression in S. cerevisiae, we estimate the noise in protein production for nearly every yeast gene, and confirm our prediction that the production of essential and complex-forming proteins involves lower levels of noise than does the production of most other genes. Our results support the hypothesis that noise in gene expression is a biologically important variable, is generally detrimental to organismal fitness, and is subject to natural selection.

Analysis of gene expression data for nearly every gene in yeast provides evidence that random variation in the production rate of proteins could significantly affect the fitness of an organism

Stochastic processes influence stationary-phase decisions in Bacillus subtilis

It has recently been proposed that phenotypic variation in clonal populations of bacterial species results from intracellular "noise," i.e., random fluctuations in levels of cellular molecules, which would be predicted to be insensitive to selective pressure. To test this notion, we propagated five populations of Bacillus subtilis for 5,000 generations with selection for one phenotype: the decision to sporulate. In support of the noise hypothesis, we report that none of the populations responded to selection by improving their efficiency of sporulation, indicating that intracellular noise is independent of heritable genotype.

Single-copy green fluorescent protein gene fusions allow accurate measurement of Salmonella gene expression in vitro and during infection of mammalian cells

We developed a reliable and flexible green fluorescent protein (GFP)-based system for measuring gene expression in individual bacterial cells. Until now, most systems have relied upon plasmid-borne gfp gene fusions, risking problems associated with plasmid instability. We show that a recently developed GFP variant, GFP+, is suitable for assessing bacterial gene expression. Various gfp+ transcriptional fusions were constructed and integrated as single copies into the chromosome of Salmonella enterica serovar Typhimurium. A comparison of the expression levels of proU-lacZ and proU-gfp+ fusions showed that GFP+ reported proU activity in individual Salmonella cells as accurately as beta-galactosidase reported activity for entire populations. The single-copy gfp+ fusions were ideal for monitoring up- and downregulation of Salmonella virulence genes. We discovered that in vitro induction of the SPI1gene prgH occurs only in a portion of the population and that the proportion varies with the growth phase. We determined the level of expression of the SPI2 gene ssaG in bacteria released from murine macrophages. Our results demonstrate for the first time that single-copy GFP+ fusions reliably report gene expression in simple and complex environments. This approach promises to allow accurate measurement of gene expression in individual bacteria during animal infection.

Programmed population control by cell–cell communication and regulated killing

De novo engineering of gene circuits inside cells is extremely difficult, and efforts to realize predictable and robust performance must deal with noise in gene expression and variation in phenotypes between cells. Here we demonstrate that by coupling gene expression to cell survival and death using cell-cell communication, we can programme the dynamics of a population despite variability in the behaviour of individual cells. Specifically, we have built and characterized a 'population control' circuit that autonomously regulates the density of an Escherichia coli population. The cell density is broadcasted and detected by elements from a bacterial quorum-sensing system, which in turn regulate the death rate. As predicted by a simple mathematical model, the circuit can set a stable steady state in terms of cell density and gene expression that is easily tunable by varying the stability of the cell-cell communication signal. This circuit incorporates a mechanism for programmed death in response to changes in the environment, and allows us to probe the design principles of its more complex natural counterparts.

Production of cell lines secreting TAT fusion proteins

Transduction of proteins and other macromolecules constitutes a potent technology to analyze cell functions and to achieve therapeutic interventions. In general, fusion proteins with protein transduction domains, such as TAT, are produced in a bacterial expression system. Here we describe the generation of a mammalian expression vector coding for TAT-EGFP fusion protein. Transfection of CHO-K1 cells by this vector and subsequent selection by Zeocin resulted in cell lines that express and secrete EGFP, a variant of the green fluorescent protein GFP. The ultimate cell line was produced by first cloning the stable integrants and subsequent selection of EGFP-expressing cells by flow cytometric sorting. In the resulting cell line approximately 98% of cells express EGFP. Using the same methodology, we generated cell lines that express DsRed fluorescent protein. The advantages of using such a mammalian expression system include the ease of generating TAT fusion proteins and the potential for sustained production of such proteins in vitro and, potentially, in vivo.

Robust control in bacterial regulatory circuits

Robust control refers to regulatory systems that are insensitive to perturbations to the intra- or extra-cellular environment. It is generally believed that most cell regulatory circuits should possess some degree of robustness. Examples of robust control and the underlying mechanisms for achieving this robustness are only beginning to be uncovered. Various forms of robustness are associated with circuits based on negative and/or positive feedback, bi-functional enzymes, protein oligomerization and discrete or continuous control.

Enhancement of the stability of genetic switches by overlapping upstream regulatory domains

We study genetic switches formed from pairs of mutually repressing operons. The switch stability is characterized by a well-defined lifetime, which grows very rapidly, albeit subexponentially, with the number of copies of the most-expressed transcription factor. The switch stability can be drastically enhanced by overlapping the upstream regulatory domains such that competing regulatory molecules mutually exclude each other. Our results suggest that robustness against biochemical noise can provide a selection pressure that drives operons together in the course of evolution.

Transcription-induced barriers to supercoil diffusion in the Salmonella typhimurium chromosome

Transcription and replication both influence and are influenced by superhelical changes in DNA. Explaining how supercoil movement is channeled in living chromosomes has been a major problem for 30 years. Transcription of membrane-associated proteins leads to localized hypersupercoiling of plasmid DNA, and this behavior indicates the presence of aberrant supercoil diffusion. Using the lambda Red recombination system, we constructed model domains in the Salmonella typhimurium chromosome to analyze supercoiling dynamics of regions encoding membrane proteins. Regulation of Tn10-derived tetracycline resistance involves a repressor, TetR, and a membrane-bound export pump, TetA. Strains deficient in TetR activity had 60-fold higher transcription levels (from P(A)) than TetR-positive strains. High tetA transcription caused a 10- to 80-fold decrease in the gammadelta resolution efficiency for the domain that includes the Tet module. Replacing tetA with genes encoding cytosolic proteins LacZ and Kan also caused the appearance of supercoil diffusion barriers in a defined region of the chromosome. In strains containing a functional TetR located next to a regulated lacZ reporter (P(R)tetR-P(A)lacZ), induction of transcription with chlortetracycline caused a 5-fold drop in resolution efficiency in the test domain interval. A short half-life resolvase showed that barriers appeared and disappeared over a 10- to 20-min span. These studies demonstrate the importance of transcription in chromosome structure and the plasticity of supercoil domains in bacterial chromosomes.

Biochemical Network Stochastic Simulator (BioNetS): software for stochastic modeling of biochemical networks

BACKGROUND

Intrinsic fluctuations due to the stochastic nature of biochemical reactions can have large effects on the response of biochemical networks. This is particularly true for pathways that involve transcriptional regulation, where generally there are two copies of each gene and the number of messenger RNA (mRNA) molecules can be small. Therefore, there is a need for computational tools for developing and investigating stochastic models of biochemical networks.

RESULTS

We have developed the software package Biochemical Network Stochastic Simulator (BioNetS) for efficiently and accurately simulating stochastic models of biochemical networks. BioNetS has a graphical user interface that allows models to be entered in a straightforward manner, and allows the user to specify the type of random variable (discrete or continuous) for each chemical species in the network. The discrete variables are simulated using an efficient implementation of the Gillespie algorithm. For the continuous random variables, BioNetS constructs and numerically solves the appropriate chemical Langevin equations. The software package has been developed to scale efficiently with network size, thereby allowing large systems to be studied. BioNetS runs as a BioSpice agent and can be downloaded from http://www.biospice.org. BioNetS also can be run as a stand alone package. All the required files are accessible from http://x.amath.unc.edu/BioNetS.

CONCLUSIONS

We have developed BioNetS to be a reliable tool for studying the stochastic dynamics of large biochemical networks. Important features of BioNetS are its ability to handle hybrid models that consist of both continuous and discrete random variables and its ability to model cell growth and division. We have verified the accuracy and efficiency of the numerical methods by considering several test systems.

Summing up the noise in gene networks

Random fluctuations in genetic networks are inevitable as chemical reactions are probabilistic and many genes, RNAs and proteins are present in low numbers per cell. Such 'noise' affects all life processes and has recently been measured using green fluorescent protein (GFP). Two studies show that negative feedback suppresses noise, and three others identify the sources of noise in gene expression. Here I critically analyse these studies and present a simple equation that unifies and extends both the mathematical and biological perspectives.

Design of genetic networks with specified functions by evolution in silico

Recent studies have provided insights into the modular structure of genetic regulatory networks and emphasized the interest of quantitative functional descriptions. Here, to provide a priori knowledge of the structure of functional modules, we describe an evolutionary procedure in silico that creates small gene networks performing basic tasks. We used it to create networks functioning as bistable switches or oscillators. The obtained circuits provide a variety of functional designs, demonstrate the crucial role of posttranscriptional interactions, and highlight design principles also found in known biological networks. The procedure should prove helpful as a way to understand and create small functional modules with diverse functions as well as to analyze large networks.

Fast evaluation of fluctuations in biochemical networks with the linear noise approximation

Biochemical networks in single cells can display large fluctuations in molecule numbers, making mesoscopic approaches necessary for correct system descriptions. We present a general method that allows rapid characterization of the stochastic properties of intracellular networks. The starting point is a macroscopic description that identifies the system's elementary reactions in terms of rate laws and stoichiometries. From this formulation follows directly the stationary solution of the linear noise approximation (LNA) of the Master equation for all the components in the network. The method complements bifurcation studies of the system's parameter dependence by providing estimates of sizes, correlations, and time scales of stochastic fluctuations. We describe how the LNA can give precise system descriptions also near macroscopic instabilities by suitable variable changes and elimination of fast variables.

Importance of input perturbations and stochastic gene expression in the reverse engineering of genetic regulatory networks: insights from an identifiability analysis of an in silico network

Gene expression profiles are an increasingly common data source that can yield insights into the functions of cells at a system-wide level. The present work considers the limitations in information content of gene expression data for reverse engineering regulatory networks. An in silico genetic regulatory network was constructed for this purpose. Using the in silico network, a formal identifiability analysis was performed that considered the accuracy with which the parameters in the network could be estimated using gene expression data and prior structural knowledge (which transcription factors regulate which genes) as a function of the input perturbation and stochastic gene expression. The analysis yielded experimentally relevant results. It was observed that, in addition to prior structural knowledge, prior knowledge of kinetic parameters, particularly mRNA degradation rate constants, was necessary for the network to be identifiable. Also, with the exception of cases where the noise due to stochastic gene expression was high, complex perturbations were more favorable for identifying the network than simple ones. Although the results may be specific to the network considered, the present study provides a framework for posing similar questions in other systems.

Is the somitogenesis clock really cell-autonomous? A coupled-oscillator model of segmentation

A striking pattern of oscillatory gene expression, related to the segmentation process (somitogenesis), has been identified in chick, mouse, and zebrafish embryos. Somitogenesis displays great autonomy, and it is generally assumed in the literature that somitogenesis-related oscillations are cell-autonomous in chick and mouse. We point out in this article that there would be many biological reasons to expect some mechanism of coupling between cellular oscillators, and we present a model with such coupling, but which also has autonomous properties. Previous experiments can be re-interpreted in light of this model, showing that it is possible to reconcile both autonomous and non-autonomous aspects. We also show that experimental data, previously interpreted as supporting a purely negative-feedback model for the mechanism of the oscillations, is in fact more compatible with this new model, which relies essentially on positive feedback.

Identifying global regulators in transcriptional regulatory networks in bacteria

The machinery for cells to take decisions, when environmental conditions change, includes protein-DNA interactions defined by transcriptional factors and their targets around promoters. Properties of global regulators are revised attempting to reach diagnostic explicit criteria for their definition and eventual future computational identification. These include among others, the number of regulated genes, the number and type of co-regulators, the different sigma-classes of promoters and the number of transcriptional factors they regulate, the size of the evolutionary family they belong to, and the variety of conditions where they exert their control. As a consequence, global versus local regulation can be identified, as shown for Escherichia coli and eventually in other genomes.